Data Analysis Techniques

What are the Goals?

  • To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data
  • To give delegates the ability to recognise which types of analysis are best suited to particular types of problems
  • To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
  • To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field
  • To introduce some basic statistical methods and concepts
  • To explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack

Description

Corporate ethos which demands continual improvement in workplace efficiencies and reduced operating, maintenance, support service and administration costs means that managers, analysts and their advisors are faced with ever-challenging analytical problems and performance targets. To make decisions which result in improved business performance it is vital to base decision making on appropriate analysis and interpretation of numerical data.

What are the Goals?

  • To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data
  • To give delegates the ability to recognise which types of analysis are best suited to particular types of problems
  • To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
  • To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field
  • To introduce some basic statistical methods and concepts
  • To explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack

Who is this Workshop for?

This workshop is suitable to a wide range of professionals but will greatly benefit:

Professionals whose jobs involve in the manipulation, representation, interpretation and/or analysis of data. Familiarity with a PC and in particular with Microsoft Excel (2003, 2007, 2010 or 2013) is assumed.

This training seminar involves extensive computer-based data analysis using Excel 2010 and therefore delegates will be expected to be numerate and to enjoy working with numerical data on a computer.

How will this Workshop be Presented?

This workshop combines theory and practice. We use company examples to illustrate how the techniques presented have been applied in real case studies. The purpose is to use a mix of proven learning techniques to ensure maximum understanding, comprehension and retention of the information presented.

The Course Content

DAY ONE: The Basics

  • Sources of data, data sampling, data accuracy, data completeness, simple representations, dealing with practical issues
  • Fundamental Statistics
  • Mean, average, median, mode, rank, variance, covariance, standard deviation, “lies, more lies and statistics”, compensations for small sample sizes, descriptive statistics, insensitive measures
  • Data Comparison
  • Correlation analysis, the auto-correlation function, practical considerations of data set dimensionality, multivariate and non-linear correlation
  • Histograms and Frequency of Occurrence
  • Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, the law of diminishing return, percentile analysis

DAY TWO – Frequency Analysis

  • The Fourier transform, periodic and a-periodic data, inverse transformation, practical implications of sample rate, dynamic range and amplitude resolution
  • Regression Analysis and Curve Fitting
  • Linear and non-linear regression, order; best fit; minimum variance, maximum likelihood, least squares fits, curve fitting theory, linear, exponential and polynomial curve fits, predictive methods
  • Probability and Confidence
  • Probability theory, properties of distributions, expected values, setting confidence limits, risk and uncertainty, ANOVA (Analysis of Variance)
  • Some More Advanced Ideas
  • Pivot tables, the Data Analysis Tool Pack, internet-based analysis tools, macros, dynamic spreadsheets, sensitivity analysis










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